Accelerated pattern matching using pattern functions

ABSTRACT

System, methods, and apparatuses enable a network security system to more efficiently perform pattern matching against data items. For example, the disclosed approaches may be used to improve the way in which a deep packet inspection (DPI) microservice performs pattern matching against data items (e.g., network traffic, files, email messages, etc.) in order to detect various types of network security threats (e.g., network intrusion attempts, viruses, spam, and other potential network security issues). A DPI microservice generally refers to an executable component of a network security system that monitors and performs actions relative to input data items for purposes related to computer network security.

TECHNICAL FIELD

Embodiments relate generally to computer network security. Morespecifically, embodiments relate to techniques for accelerating patternmatching processes used by deep packet inspection (DPI) tools and othercomputer network security functions.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

The vast majority of organizations today rely on computer systems andnetworks for an increasingly wide variety of business operations. As thereliance on these systems networks has grown, so too has the importanceof securing those computer systems and networks against internal andexternal security threats. However, the breadth and complexity ofsecurity threats targeting such computer systems and networks is far andwide and ever growing. To monitor and address these security threats,organizations increasingly rely on sophisticated computer networksecurity applications and hardware such as firewalls, anti-virus tools,data loss prevention software, etc.

Some types of computer network security applications involve deep packetinspection (DPI). At a high level, DPI involves monitoring networktraffic for instances of viruses, spam, network intrusion attempts,protocol non-compliance, etc., by searching for patterns in the dataportion, headers, and other protocol structures comprising networktraffic. For example, a DPI process may monitor incoming and outgoingnetwork traffic for patterns known to correspond to malicious orunwanted network traffic and block any traffic containing one or more ofthe known patterns. A benefit of using DPI to monitor computer networktraffic in this way is that a network security application can“understand” and monitor the use of certain network protocols and higherlayer applications (e.g., HTTP, email, etc.) which may span multiplenetwork packets, whereas other packet filtering techniques may operateonly on individual packets. However, as the number of patterns to bedetected in network traffic increases, the computational complexity ofchecking potentially vast amounts of network traffic and other data forthe existence of such patterns can quickly lead to undesirableperformance delays.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 is a block diagram illustrating a security service configured tomonitor traffic sent among an application and one or more serversthrough a routing network in accordance with the disclosed embodiments;

FIG. 2 is a block diagram illustrating a flow of application datathrough a stateless processing, fault-tolerant microservice environmentin accordance with the disclosed embodiments;

FIG. 3 is a block diagram illustrating example components of a DPIprocessing microservice in accordance with the disclosed embodiments;

FIGS. 4, 5 are block diagrams illustrating an example pattern table andan example class table, respectively, in accordance with the disclosedembodiments;

FIG. 6 illustrates a pattern state diagram and a corresponding patternstate table each representing a particular pattern in accordance withthe disclosed embodiments;

FIG. 7 is a flow diagram illustrating an example process for using apattern state table to determine whether a pattern exist in a data itemin accordance with the disclosed embodiments;

FIG. 8 illustrates a partial pattern state diagram representing a mergedstate diagram corresponding to three separate patterns in accordancewith the disclosed embodiments;

FIG. 9 is a flow diagram illustrating an example process for generatingand using a merged pattern state table to determine whether one or morepatterns exist in a data item in accordance with the disclosedembodiments;

FIG. 10 illustrates an example of an enhanced pattern state tableincluding a callback function identifier field in accordance with thedisclosed embodiments;

FIG. 11 is a flow diagram illustrating an example process for generatinga master pattern matching table, an alternative master pattern matchingtable, and a plurality of class pattern matching tables in accordancewith the disclosed embodiments;

FIG. 12 is a flow diagram illustrating a process for performing patternmatching using a master pattern matching table, an alternative masterpattern matching table, and/or a plurality of class pattern matchingtables in accordance with the disclosed embodiments;

FIGS. 13, 14 are block diagrams illustrating another example patterntable and an example class table, respectively, in accordance with thedisclosed embodiments;

FIG. 15 is a block diagram illustrating components of an example DPIprocessing microservice in accordance with the disclosed embodiments;

FIG. 16 is a block diagram illustrating an example function table inaccordance with the disclosed embodiments;

FIG. 17 is a flow diagram illustrating an example process for generatinga set of pattern functions and a separate pattern matching table from aset of patterns in accordance with the disclosed embodiments;

FIG. 18 illustrates separate examples of searching for a variable offsetpattern and a fixed offset pattern in a data item in accordance with thedisclosed embodiments;

FIG. 19 is a flow diagram illustrating an example process of searchingfor patterns in a data item using both regular expression matching andpattern functions in accordance with the disclosed embodiments;

FIG. 20 illustrates a computer system upon which an embodiment may beimplemented.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thepresent invention.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment need not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Embodiments are described herein according to the following outline:

1.0. General Overview

2.0. Operating Environment

-   -   2.1. System Overview    -   2.2. Deep Packet Inspection (DPI) Microservices

3.0. Functional Overview

-   -   3.1. Pattern Matching Overview    -   3.2. Pattern Matching Using State Leaps    -   3.3. Alternative Pattern Matching Tables    -   3.4. Pattern Matching Using Pattern Functions

4.0. Example Embodiments

5.0. Implementation Mechanism—Hardware Overview

6.0. Extensions and Alternatives

1.0. General Overview

Modern data centers and other computing environments can includeanywhere from a few computer systems to thousands of systems configuredto process data, service requests from remote clients, and performnumerous other computational tasks. The large number of interworkingsystems, applications, etc., make such computing environmentssusceptible to a wide variety of network security issues. A number ofnetwork security tools are available to protect such systems and thecomputer networks interconnecting these systems, and many of these toolscomprise a monolithic set of network security functions. For example, atypical network security tool might comprise a hardware unit includingfirewall services, routing services, virtual private network (VPN)services, etc.

The type of network security tool described above is useful forproviding a variety of network security functions as a single unit.However, efficiently scaling these types of network security tools isoften challenging. For example, if a particular computer environmentmight benefit from increased firewall resources, a system administratormay install one or more additional hardware units each includingfirewall services in addition to a suite of other network securityfunctions. While the addition of these new hardware units may meet theincreased firewall resource needs, some of the hardware units mayinclude unnecessary and/or underutilized resources devoted to virtualprivate network (VPN) services, data loss prevention (DLP) services, orother security services.

One way in which many modern computing environments scale resources moreefficiently is with the use of virtualized computing resources. Avirtualized computing resource generally refers to an emulated computersystem that, like a physical computer, runs an operating system andapplications, but may also use the same physical resources as one ormore other virtualized resources. According to one embodiment, thesetypes of virtualized infrastructures can be used to efficiently scalenetwork security applications with the use of “microservices,” where amicroservice represents a particular type of virtualized computingresource packaged as a software container. For example, separatemicroservices may be created to provide firewall resources, DLPservices, VPN services, etc. In general, the use of such microservicescan provide greater flexibility because the microservices can be easilydeployed and scaled in response to variable demands for various networksecurity services.

The type of efficient network security application scaling describedabove can be achieved with the use of a next generation softwarefirewall that is configured to scale network security services usingmicroservices. Although many of the techniques described herein areexplained with reference to a microservice-based network securityapplication, the techniques are also applicable to other types ofnetwork security systems.

2.0. Operating Environment

2.1. System Overview

FIG. 1 is a block diagram illustrating a networked computer environmentin which an embodiment may be implemented. FIG. 1 represents an exampleembodiment that is provided for purposes of illustrating a clearexample; other embodiments may use different arrangements.

The networked computer system depicted in FIG. 1 comprises one or morecomputing devices. These one or more computing devices comprise anycombination of hardware and software configured to implement the variouslogical components described herein. For example, the one or morecomputing devices may include one or more memories storing instructionsfor implementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In one embodiment, one or more security services 110 may be configuredto monitor network traffic and other data sent between an application116 and one or more servers 104, 106 through a routing network 108. Thesecurity service 110 comprises one or more “microservices” used tomonitor and perform various actions relative to data items (e.g. networktraffic, files, email messages, etc.) sent to and received from one ormore applications 116 and servers 104, 106. The microservices comprisingsecurity service 110 do not need to be confined to one physical serversuch as a server 104, 106. For example, one or more microservices of thesecurity service 110 may be executed on server 104 and othermicroservices of the security service 110 are executed on 106. In someembodiments, the security service 110 is executed on a different serverfrom one or more servers for which the security service is responsiblefor monitoring and protecting.

In an embodiment, a routing network 108 provides connectivity amongservers 104, 106, security service 110, and application 116. In someembodiments, routing network 108 is partially configured responsive tohypervisor configuration of servers 104 and 106. In some embodiments, arouting network 108 is partially or entirely configured responsive tohypervisor configuration of servers 104 and/or 106.

In one embodiment, by virtue of routing information included in channeldata encapsulation packets, data traveling between an application 116and server 104 and/or server 106 is routed to the correct server, and iskept separate from data traveling between the application 116 and theother server. Accordingly, what is essentially a private network 112 maybe created between the server running security service 110 and server104. Similarly, what is essentially a private network 114 may be createdbetween the server running security service 110 and server 106.

FIG. 2 is a block diagram illustrating a flow of application datathrough a stateless processing, fault-tolerant microservice environmentin accordance with disclosed embodiments. As illustrated, securitysystem 200 includes interface microservices 202, 204, and 206, TCP/IPmicroservices 210 and 212, and DPI microservices 220, 222, and 224.Other examples include a different number of microservices and/or adifferent number of microservice types. In the example of FIG. 2, aninterface microservice 202 receives packet A 208, and generates acontext X 260.

One benefit of the security system illustrated in FIG. 2 is the handlingof state. For example, if packets belong to a certain context X, thesecurity system 200 may enable both TCP/IP microservices 210 and 212 toperform meaningful work on the packets. By implementing TCP/IPprocessing as microservices 210 and 212 with an external state structureand a context that accompanies processed data, each TCP/IP microservice,and any other microservice at every level of the security hierarchy, canbe isolated from other microservices and can be scaled independently.Each microservice can access the state for any packet or reassembledpacket data, thereby enabling real-time load balancing. In many cases,the context enables microservices to forego consulting service state(state associated with processing at the hierarchy level of the specificmicroservice), thereby reducing the demands on the global staterepository.

As an example, consider the context 262 obtained by TCP/IP microservice210 as part of packets received from interface microservice 202 astransmission 240. Context 262, when transmitted to DPI microservice 220as part of transmission 242 along with the reassembled packet data,contains information that may enable the DPI microservice to forego orsimplify processing of this reassembled data. Such information caninclude, for example, a context bit or field specifying a subset ofregular expressions or patterns to be used for DPI processing, a numberof bytes of reassembled data to be received before beginning DPIprocessing, specific allowed or disallowed protocols, and otherinformation potentially avoiding a DPI state lookup.

In an embodiment, microservices of a security system 200 are stateless.For example, each of the microservices may retrieve state informationfrom an outside source such that the microservice can process packets orcontent belonging to any context. Each microservice may retrieve andupdate service state (that state associated with the microserviceprocessing). Additionally, each microservice may retrieve and updatecontext state (state associated with the context relevant for allsecurity service processing). In some embodiments, the process state andcontext state share a global state service. Examples of elements ofcontext state include a level of suspicion regarding traffic from asource IP, a policy to ignore certain ports or protocols and otherinformation used to process the packets, reassembled content, andextracted objects from communication identified with the context.

In an embodiment, multiple microservices in the same or differenthierarchy of the security system may be able to process packetsassociated with the same context at the same time. If one securitymicroservice fails (e.g., if a TCP microservice fails to respond to arequest), another microservice can take over and process the requestusing the failed microservice's context.

Returning to the example of FIG. 2, the generation of context X 260 mayinclude considering properties associated with packet A 208 (e.g., suchas an n-tuple detailing routing information), and also a state lookup ora context lookup, in addition to other information. Interfacemicroservice 202 provides packet A 208 and context X 260 to TCP/IPmicroservice 210 or 212 via path 240 or 250, respectively. For example,interface microservice 202 may conduct a load-balancing to select one ofthe TCIP/IP microservices to forward the packet A 208 and the context X260.

In an embodiment, TCP/IP microservices 210 and 212 are stateless, butmay benefit from the context X generation performed by interfacemicroservice 202. For example, whichever of TCP/IP microservices 210 and212 receives packet A may disassemble the packet to extract the dataassociated with the packet and conduct security processing on the data.TCP/IP reassembly generally consists of associating packets with flows(e.g., identified by source and destination IP and port values) andusing the TCP sequence numbering to place the packets into a correctorder, remove any overlap or duplication, and/or identify missing or outof order packets.

In FIG. 2, TCP/IP microservices 210 or 212 forwards the extracted dataand/or the data resulting from the security processing to DPImicroservice 220 via paths 242 or 252, respectively. Along with thetransmitted data, TCP/IP microservice 210 or 212 forwards context X 262or 264, respectively, to a DPI microservice 220. In some embodiments,context X 260, 262, 264, and 266 are substantially identical.

In an embodiment, DPI microservice 220 is also stateless and may use thecontext provided by TCP/IP microservice 210 or 212 in transmission 242or 252. DPI microservice 220 may load DPI processing state beforeprocessing the received data, but can perform some work (e.g.,scheduling different DPI pattern state tables) based on the context.Transmitting the context to the DPI microservice therefore may obviatesome amount of work by the DPI microservice. If TCP/IP microservice 210fails and interface microservice 202 instead utilizes TCP/IPmicroservice 212, DPI microservice 220 may obtain the context from thetransmission of reassembled TCP content in transmission 252.

Although FIG. 2 does not show a second packet, when a subsequent packetassociated with the same context is received, interface microservice 202may conduct a load balancing and select one of the TCP/IP microservicesto forward the packet along with context X 260. In one embodiment,interface microservice 202 chooses to forward the second packet toTCP/IP microservice 212 via path 250. TCP/IP microservice 212 performssome security processing, then transmits the second packet and context X264 to DPI microservice 220 via path QAF52. After performing somesecurity processing, DPI microservice 220 responds to TCP/IPmicroservice 212 via path 254, and TCP/IP microservice responds tointerface microservice 202 via path 256.

Summarizing the operation of an embodiment as illustrated by FIG. 2, aninterface microservice transmits packets to a TCP/IP microservice alongwith a context that has been generated based on the contents of thepackets. The transmission comprises a request to perform a securityservice (e.g., TCP/IP reassembly) for the packets to generatereassembled data. The TCP/IP microservice consults the received contextto determine whether to obtain a context state, service state, or both,from a state repository to perform the security service. Reassembly isperformed by the TCP/IP microservice, any modified state returned to thestate repository and the reassembled data transmitted, along with thecontext, to a DPI microservice as a request to perform DPI processing.

Continuing the example illustrated by FIG. 2, the DPI microservicereceives the reassembled data and context from the request to performDPI security services transmitted by the TCP/IP microservice. The DPImicroservice consults the received context to determine whether toobtain a context state, service state, or both, from a state repositoryto perform its security service. DPI inspection may be performed by theDPI microservice, any modified state returned to the state repository,and a response sent to the TCP/IP microservice.

2.2. Deep Packet Inspection (DPI) Micro Services

FIG. 3 is a block diagram illustrating example components of a DPImicroservice. In an embodiment, a security service 306 comprises a DPImicroservice 310, which further comprises a pattern processor 320, apattern matching table 322, a pattern table 330, a class table 332, anda function table 334. For example, the security service 306 maycorrespond to the security service 306 depicted in FIG. 1, where the DPImicroservice 310 is one of a possible plurality of microservices runningwithin the security service 306. For example, although not depicted, thesecurity service 306 may also include one or more data loss prevention(DLP) microservices, TCP/IP microservices, etc. FIG. 3 represents anexample embodiment that is provided for purposes of illustrating a clearexample; other embodiments may use different arrangements.

According to an embodiment, a DPI microservice 310 generally representsa module for performing deep packet inspection on data items including,for example, network messages, email messages, files, etc., sent amongapplication(s) 302 and/or server(s) 304. In one embodiment, the DPImicroservice 310 represents a software “container,” where a containerrepresents an isolated user space instance within a virtualizationenvironment in which the kernel of an operating system allows for theexistence of multiple isolated user-space instances. In other examples,the DPI microservice 310 may represent a different type of virtualmachine instance, a thread of execution, a standalone softwareapplication, or any other type of computing module. In some embodiments,DPI functionality of a security service 306 is provided by a pluralityof DPI microservices, wherein the number of microservices in operationat any given time may be scaled to meet the DPI processing requirementof the traffic processed by security service 306.

In an embodiment, a pattern processor 320 represents a process forperforming pattern matching and other related functions in conjunctionwith one or more of a pattern matching table 322, a pattern table 330, aclass table 332, and/or a function table 334. In one embodiment, apattern table 330 stores, among other information, a set of patterns tobe searched for in data items received by the DPI microservice 310. Forexample, the set of patterns stored in a pattern table 330 may includepatterns known to be frequently present in malicious types of networktraffic, spam email messages, viruses, etc. In other examples, somepatterns stored within pattern table 330 may indicate a probability of asecurity event such that a plurality of patterns are used to confirm thepresence of said security event.

In an embodiment, the set of patterns stored in a pattern table 330 formthe basis for one or more pattern matching table(s) 322, where a patternmatching table 322 represents a set of pattern matching states thatpattern processor 320 uses to determine the presence of one or morepatterns in input data items. Pattern processor 320 may use a patternmatching table 322 by applying the data to be scanned, one letter, byteor other portion at a time, as a lookup, along with a current state, togenerate a next state. In one embodiment, one or more of the patterns ina pattern table 330 represents “regular expressions,” where a regularexpression is based on a particular language for defining sequences ofcharacters to define search patterns. If one or more of the patterns areexpressed as regular expressions, for example, a corresponding patternmatching table 322 may be referred to as a regular expression matchingtable, where a regular expression matching table may be used by thepattern processor 320 to perform regular expression matching of patternsagainst input data items.

As used herein, regular expression matching refers to a process ofdetermining whether one or more patterns defined by one or more regularexpression, and contained within a pattern table 330, are present ininput data items. Although some of the examples described herein areexplained with reference to regular expressions and regular expressionmatching, the techniques are also applicable to other types of patternmatching. As used herein, a pattern may be a regular expression in theform used for specification within a pattern table 330. In the case ofmost textual patterns, the pattern and corresponding regular expressionmay be the same. Regular expressions are generally standardized (such asPOSIX regular expressions or Perl Compatible Regular Expressions (PCRE)whereas patterns may be implementation specific or be a superset ofstandardized regular expressions.

In one embodiment, a class table 332 comprises a set of class entries,where each class entry specifies a set of one or more patterns from thepattern table 330 which are members of the class. Each class, forexample, may include patterns relating to similar types of data items,similar types of data item content, similar types of patterns, etc. Forexample, one pattern class may include patterns relating to detectingnetwork security issues found in HTTP messages, while another classincludes patterns relating to detecting a particular type of spam emailmessage, and so forth.

In one embodiment, a function table 334 comprises a set of entries eachcomprising and/or identifying a pattern function. At a high level, apattern function is an executable code segment configured to acceleratea process for matching certain patterns against input data items. Ingeneral, each pattern function may be configured to search data itemsfor patterns that specify one or more fixed offsets within a data itemat which a particular pattern may be located (referred to herein as“fixed offset patterns”). For example, one particular pattern mayspecify a numerical value pattern known to exist, if at all, at one ormore particular locations within a certain type of network message(e.g., as a value for one or more particular fields within an HTTPmessage). In this instance, instead of searching for the pattern at alllocations within input data items, a more efficient pattern function maybe created and which is configured to search for the presence of thepattern only at the one or more particular locations specified by thepattern syntax. In addition to a pattern functions ability to moreefficiently search for fixed offset patterns, by removing these patternsfrom a pattern table 330 to a function table 334, the speed with whichthe pattern processor 320 can process data items using the patternmatching table 322 may increase.

Regular expressions and other types of patterns may contain fixed orvariable offsets that require a specific number or range of inputscharacters to exist from an anchor in the data stream for a match. Asexamples, a regular expression may require a pattern “ABC” to be presentat the beginning of a data stream, at least X characters from the starta data stream or within X characters of another regular expression.Fixed offset patterns are those patterns for which the position withinthe data stream to search for a pattern can be determined to be lessthan the length of the data stream itself. This includes, but is notlimited to, patterns at an exact offset in the data stream, patterns atleast X characters after the start of the data stream, patterns betweenoffsets X and Y in the data stream and other pattern limitations whereinthe full data stream need not be compared.

The creation and use of pattern matching tables 322, pattern tables 330,class tables 332, and function tables 334, among other components, isdescribed in more detail hereinafter.

3.0. Functional Overview

Approaches, techniques, and mechanisms are disclosed that enable anetwork security system to more efficiently perform pattern matchingagainst input data items. For example, the approaches described hereinmay be used to improve the way in which a deep packet inspection (DPI)microservice performs pattern matching against data items (e.g., networktraffic, files, email messages, etc.) in order to detect various typesof network security threats (e.g., network intrusion attempts, viruses,spam, and other potential network security issues). As used herein, aDPI microservice generally refers to an executable component of anetwork security system, such as the system described in Section 2.0,that monitors and performs actions relative to input data items for avariety of network security related purposes. As illustrated in FIG. 3,for example, a DPI microservice 310 may be a component of a securityservice 306, where the DPI microservice 310 is one instance of networksecurity microservice among a possible plurality of other microservices.

3.1. Pattern Matching Overview

The network security functions performed by a DPI microservice and othernetwork security services may involve “pattern matching” a set ofpatterns against input data items. In this context, pattern matchinggenerally refers to a process for determining whether a given sequenceof input tokens (e.g., a sequence of characters, bytes, or otherelements of an input data item) contains one or more defined patterns(e.g., token sequences corresponding to words, phrases, byte sequences,or other patterns of interest). As one example, in the context of spamdetection, a DPI microservice may use pattern matching to determinewhether incoming email messages contain one or more defined patternspotentially known to correlate with spam messages (e.g., thewords/phrases “stocks”, “eliminate debt”, “order now”, etc.). Ininstances where the patterns to be matched are represented as regularexpressions, the pattern matching may be referred to as regularexpression matching.

To further illustrate an example of how a DPI microservice may usepattern matching to detect network security threats, consider an examplewhere a DPI microservice includes a list of several hundred or thousandsof words and phrases known to commonly appear in spam email messages.The DPI microservice may be configured to receive incoming emailmessages for a network of computer systems and to determine whether eachemail message contains one or more of the predefined words and/orphrases, where the determination is made by matching a patternassociated with each word and phrase against the email message. Forexample, if one of the words is “stocks”, then the DPI microservice maydetermine whether the sequence of characters “s”, “t”, “o”, “c”, “k”,and “s” is present at any location within incoming email messages.

Based on the example pattern matching process described above, forexample, a DPI microservice may be configured flag email messages thatcontain some number and/or combination of the predefined words and/orphrases as potential spam messages. For example, if the DPI microservicedetects the presence of the phrase “special promotion” in an email, theDPI microservice may flag the email as spam. As another example, if theDPI microservice detects each of the words “stock,” “investment,” and“guarantee,” then the combination of those words may cause the DPImicroservice to flag the email as spam. Techniques such as Bayesiananalysis may be used to assign weights or probabilities to individualpatterns and to sum those weights or probabilities to make adetermination regarding the processed data.

In order to determine whether a data item contains one or morepredefined patterns in a reasonable amount of time, a DPI microservicemay be configured to scan input data items in one pass for all of thepatterns. For example, if a DPI microservice is configured to detect thepresence of one or more of the patterns “stock,” “investment,” and“guarantee,” the DPI microservice may be configured to scan eachincoming email message in one pass for all three patterns at the sametime instead of scanning incoming messages for each pattern separately.As described in more detail hereinafter, in one embodiment, a DPImicroservice may scan data items for a plurality of patterns in a singlepass by representing the plurality of patterns as a single finite statemachine and/or state transition table against which the data items areprocessed. However, analyzing and searching a large number of incomingdata items using a state-based representation of many different patternspresents a number of challenges due in part to the complexity of suchstate-based representations as the number of patterns to be checkedincreases.

The complexity of searching data items for a large number of patterns isincreased even further when the patterns include not only simple stringsbut also variable patterns. For example, one pattern may specify asearch for dates of the form “??/??/????”, where each of the “?”characters of the pattern represents a variable numeric character. Inthis example, the defined pattern may match any of the strings such as“04/18/1954”, “01/10/2009”, and “44/66/5000”, but may not match “Apr.18, 1954” or “Wednesday”. More sophisticated patterns could also bedefined to ensure that only valid dates matching the pattern aredetected (e.g., such that the string “01/10/2009” is detected, but aninvalid date such as “99/99/3000” is not matched).

One way in which both simple and variable patterns can be expressed iswith regular expressions. At a high level, a regular expressioncomprises a sequence of characters that define a search pattern. Whileregular expressions may define search patterns for simple words andphrases (e.g., a search pattern for the word “pear” may be specifiedsimply by the regular expression “pear”), regular expressions may alsoinclude other syntax that enable specifying searches for variablepatterns. For example, if it is desirable to detect the presence ofvalidly formatted email addresses in data items, a regular expressionsearch pattern for any email address may be expressed as“/^([a-z0-9_\.−]+)@([\da-z\.−]+)\.([a-z\.]{2,6})$/”, where this regularexpression matches any mailbox name followed by the “@” character,followed a domain name.

Different standards may extend the capability of regular expressions atthe cost of more processing complexity. The complexity (and resourcerequirement) for processing regular expressions grows rapidly with boththe number of expressions and the complexity of the individualexpressions.

FIG. 4 is a block diagram illustrating a pattern table comprising aplurality of pattern entries, each pattern entry specifying informationabout a particular pattern. For example, a pattern table 402 may store aset of patterns for which a DPI microservice 310 is to search in dataitems received by the microservice. In one embodiment, a pattern table402 comprises one or more pattern entries (e.g., pattern entries 410 . .. 420), where each pattern entry comprises a pattern name, a patternsyntax, a pattern state table, and a pattern class list. For example, inreference to the pattern entry 410, the pattern entry comprises apattern name 412, a pattern syntax 414, a pattern state table 416, and apattern class list 418. The structure of the pattern table 402 isprovided for illustrative purposes only; in other examples, patternsused by a DPI microservice may be stored in other ways and associatedwith fewer or more data fields.

In an embodiment, a pattern name (e.g., pattern name 412) represents ahuman-readable label for an associated pattern. For example, if thepattern entry 410 relates to detecting security threats in incomingemail messages, the pattern name 412 may be “Clickbait” or “Scam”). Asanother example, if the pattern 420 relates to detecting malicious HTTPrequest messages, the pattern name 422 may be “HTTP traffic”. Eachpattern may have a unique pattern name or, in other examples, somepatterns may share portions of a common pattern name such as a prefix,suffix or other portion.

In an embodiment, a pattern syntax (e.g., pattern syntax 414) specifiesa sequence of tokens or other syntax used to define the associatedpattern. As described above, a pattern syntax may define a staticcharacter sequence, a variable character pattern, variable lengthpatterns, a particular byte sequence, or any other type of pattern. Inone embodiment, a pattern syntax may comprise a regular expression,where a regular expression comprises a sequence of characters thatdefine a particular search pattern. As described above, a regularexpression may specify a static string of letters and/or may alsoinclude other more sophisticated syntax for finding patterns havingvariable characters, length, arrangements, etc. In other examples, apattern syntax may comprise other types of grammars, parsing languages,etc., to define one or more patterns.

In an embodiment, a pattern state table (e.g., pattern state table 416),also referred to herein as a pattern matching table, comprises a datastructure representing a process of searching for the pattern as afinite state machine. At a high level, a finite state machinerepresentation of a pattern comprises a defined set of states andtransitions among the set of states, where arrival at one or more of thedefined states represents a pattern match. For example, acharacter-based data item may be processed one character at a time insequence, where each received character is used to determine a nextstate transition. In general, a pattern state table or other similartype of data structure may be used to represent a pattern in a formatthat is more suitable for processing by a DPI microservice or otherprocess.

In an embodiment, a pattern class list (e.g., pattern class list 418)specifies zero or more “classes” to which the corresponding patternbelongs, where each class represents a grouping of one or more patterns.A set of patterns may be grouped into a particular class, for example,because the patterns relate to similar types of data items, detectsimilar types of patterns, or based on any other characteristics.

FIG. 5 is a block diagram illustrating a class table comprising aplurality of class entries, each class entry specifying informationabout a particular “class” of patterns. As indicated above, each patternclass refers generally to a grouping of patterns (e.g., patterns from apattern table 402). A set of one of more patterns may belong to aparticular class because the patterns relate to similar types of inputdata items (e.g., one class may include patterns frequently found inemail messages, another class may include patterns frequently found inHTTP messages, etc.), relate to similar subject matter found within dataitems (e.g., one class may include patterns which frequently occur in“phishing” attempts, another class may include patterns that relate tofinancial information, etc.), relate to similar types of patterns (e.g.,one class may include patterns which detect various date formats,another class may include patterns which detect similarly structurednetwork protocol messages, etc.), or based on any other groupingcharacteristics.

In an embodiment, each class entry comprises a class name (e.g., classname 512 for the pattern class entry 510), a class entry mask (e.g.,class entry mask 514), a class callback function (e.g., class callbackfunction 516), and a pattern name list (e.g., pattern name list 518). Aclass name, for example, may represent a human-readable label for theassociated class. In an embodiment, a class entry mask may represent apattern or set of patterns which, when encountered in a data item beingprocessed, causes the associated class callback function to be invoked.In an embodiment, a pattern name list includes a set of identifierswhich identify each of the patterns (e.g., from a pattern table 402)belonging to the class. Additional details regarding the use of classtables to perform an accelerated pattern matching process are describedin subsequent sections.

FIG. 6 illustrates an example pattern state diagram and a correspondingpattern state table, each representing the same particular pattern 602.In the example of FIG. 6, a pattern 602 defines a sequence of charactertokens corresponding to the word “PEAR”. The defined pattern 602 may beone of many different patterns which a DPI microservice or othercomponent is configured to search for in various input data items. Forexample, a DPI microservice may receive one or more network trafficmessages, email messages, or file attachments, etc., and determinewhether the pattern “PEAR”, in addition to possibly many other patterns,is present in any part of the input data items.

As indicated above, a pattern, such as the pattern 602 defined by thepattern syntax “PEAR”, may be represented as a finite state machine, asillustrated by the pattern state diagram 604. The pattern state diagram604, for example, include one or more nodes, each representing aparticular state, and one or more vertices connecting the nodes, whereeach vertex represents a transition from one state to another. Forexample, based on the states and state transitions represented in FIG.6, a pattern processor 320 may start at a first state, and process aninput data item one input element at a time in sequence, transitioningamong the states according to the next character, to determine whetherthe corresponding pattern appears in the data item.

In the example of pattern state diagram 604, which is configured todetermine whether the pattern “PEAR” exists in input data items,processing may begin at the “null” node 612. An input data item may thenbe processed one character, byte, or other data item unit at a time, andthe current state may be updated based on each next character. Accordingto pattern state diagram 604, for example, if the current state is the“null,” and the next input character is a “P” character, a transition ismade to the “P” state represented by node 614; otherwise, if the nextcharacter is any other character, the processing remains in the “null”state at node 612. If the current state is the “P” state and the nextinput character is an “E” character, a transition is made to the “E”state represented by the node 616; if the next input character isinstead another “P” character, the processing remains at the “P” staterepresented by node 614; otherwise, if the next character is any othercharacter, the processing returns to the “null” state at node 612. Theprocessing continues in this manner transitioning from a current stateto a next state based on subsequent input characters until no additionalcharacters remain, until a pattern match is detected, or until someother condition occurs. In this way, the only way to reach the state “R”state represented by the node 620 is for the characters “P”, “E”, “A”,and “R” to appear in that order, corresponding to an occurrence of thepattern “PEAR” in an input data item. As described in more detailhereinafter, the arrival at a particular state (e.g., arriving at the“R” state represented by the node 620) may signal a pattern “match”indicating that the pattern corresponding to the state diagram wasdetected in an input data item.

In one embodiment, a pattern state table 610 illustrates an example datastructure representing the same finite state machine represented by thepattern state diagram 604. Similar to pattern state diagram 604, thepattern state table 610 comprises a set of table entries whichcollectively define the various states and state transitions involved indetermining whether the pattern syntax “PEAR” exists in input dataitems. In general, a pattern state table may represent a way to store acorresponding finite state machine in memory of a computing device andwhich can be used by a DPI microservice to perform pattern matching.

In an embodiment, a pattern state table 610 comprises a plurality oftable entries, each table entry specifying a current state 632, an inputvalue 634, a next state 636, and a match indicator 638. For each currentstate 632, one or more next state 636 values correspond to a set ofpossible next input characters. For example, if the processing iscurrently at the “P” state, the set of possible next input charactersfor the purposes of matching the pattern “PEAR” includes a “P”, an “E”,or “default” (any other character), where each of the possible nextinput characters is associated with a transition to a particular nextstate. Referring again to the example where the processing is currentlyat the “P” state, if the next input character is an “E” character, thecorresponding entry of the pattern state table 610 specifies thatprocessing transitions to the “E” state.

In one embodiment, each table entry in the pattern state table 610further includes a match indicator 638, where a match indicatorindicates whether the state represented by the corresponding table entryrepresents a complete pattern match, or whether the state represents anintermediate state for matching one or more patterns. For example,because the pattern state diagram 604 represents the state diagram forthe pattern syntax “PEAR”, the table entry corresponding to thetransition from the “A” state to the “R” state indicates that a matchhas occurred since this state transition occurs only if the fullsequence of characters “PEAR” is detected in an input data item.

FIG. 7 is a flow diagram illustrating an example process for using apattern state table to determine whether a pattern exists in an inputdata item. Although the flow diagram of FIG. 7 refers to “character”input, the example process is equally applicable to other types ofnon-character input.

At block 702, a next input character is received. For example, if aparticular data item being processed comprises a HTTP request messageincluding the line “GET/pub/WWW/TheProject.html HTTP/1.1”, the messagemay be processed incrementally by receiving the first character “G”,followed by the next character “E”, followed by the next character “T”,followed by a space character, and so forth, where each next characteris received during a separate iteration of the example process depictedin blocks 702-708.

At block 704, based on the next input character received at block 702and a current state, a next state is identified in a pattern statetable. For example, the pattern state table may be similar to thepattern state table 610 depicted in FIG. 6. As depicted in pattern statetable 610, for example, if the current state is “E” (indicating that theprevious character input received was the character “E”), and the nextinput character is an “A”, the pattern table may be searched to identify“A” as the next state (because the pattern table includes an entry whichspecifies a current state “E”, an input value of “A”, and a next stateof “A”).

At block 706, if the next state corresponds to a pattern match, theoccurrence of the matched pattern is signaled. Referencing the examplepattern state table 610 again, the occurrence of a matched pattern maybe signaled if the “match” field is set to “yes” for the next stateidentified in block 704. In an embodiment, signaling a matched patternmay include setting a match flag, incrementing a counter, adding thematched pattern to a matched pattern list, causing display of one ormore visual alerts, and/or performing any other processes.

At block 708, the current state is updated. For example, based onidentifying the next state in the pattern state table at block 704, thecurrent state may be set to the identified next state for subsequentprocessing steps. In an embodiment, the process illustrated in blocks702-708 may be repeated until there are no additional input characters,until one or more particular patterns are matched, or until any otherdefined conditions are met.

FIG. 8 illustrates an example of a partial pattern state diagram,similar to the pattern state diagram 604 depicted in FIG. 6,representing a combined set of states and state transitionscorresponding to a search for the presence of any of three separatepatterns in a single pass through a data item. In particular, thepartial pattern state diagram 810 illustrates an increase in the numberof state transitions when searching for each of the patterns “PEAR”,“APPLE”, and/or “ORANGE”, as compared to the pattern state diagram 604searching for only the single pattern “PEAR”. For example, the partialpattern state diagram 810 depicts a large number of state transitionsinvolving the state “A2” represented by the node 812. The large numberof state transitions involving the state “A2” is due in part to each ofthe patterns 804-808 including one or more “A” characters, where receiptof a next character input of “A” may possibly represent a part ofmatching any of the three patterns. The complexity of a state diagramrepresenting a search for multiple patterns may increase even furtherwhen some or all of the patterns involve variable components.

In one embodiment, generating a combined pattern state diagram and/orcombined pattern state table for a plurality of patterns may involve“merging” the set of state transitions representing a search for each ofthe patterns individually. For example, a complete set of states andstate transitions for the partial pattern state diagram 810 may begenerated by separately generating a complete pattern state table foreach of the patterns “PEAR”, “APPLE”, and “ORANGE”, and then merging thestate tables into a single combined state table. The overlap ofconstituent characters and possible states of partial match generallyincreases substantially as the number of patterns grows.

FIG. 9 is a flow diagram illustrating an example process for generatinga combined pattern state table for a plurality of patterns, and forusing the combined pattern state table to search for one or more of theplurality of patterns in input data items. At block 902, a separatepattern table is generated for each pattern of the set of patterns. Forexample, each pattern of a set of input patterns may be represented as aset of state and state transitions which determine whether the patternis matched based on an input character sequence, as described above inreference to FIG. 6-8. At block 904, a master pattern table is createdby merging together all of the separate pattern tables. For example, theset of states and state transitions comprising each individual patternmatching table may be combined into a single “master” pattern matchingtable, where the master pattern matching tables indicates when a matchoccurs for any of the patterns included in the master table. At blocks906-912, steps similar to those described above in reference to FIG. 7may be performed to determine the presence of one or more of thepatterns represented in the master pattern matching table based on aninput data item.

3.2. Pattern Matching Using State Leaps

As illustrated above, the complexity of a pattern matching table andcorresponding pattern matching process may increase significantly as thenumber of search patterns increases. According to an embodiment, aprocess for pattern matching a set of patterns can be accelerated bydynamically “state leaping” among a set of pattern classes, where eachpattern class represents a defined subset of the complete set ofpatterns. As used herein, a “state leap” generally involves searching adata item for a set of patterns represented by one pattern matchingtable and, in response to detecting the presence of one or moreparticular patterns from the pattern matching table in a first portionof a data item, selecting another pattern matching table to use forprocessing a second portion of the data item. For example, a patternmatching process may begin processing a data item using a first patternmatching table and detect one or more particular patterns early on andwhich indicate that the data item likely represents a particular type ofHTTP message. In response to detecting the one or more particularpatterns in the first portion of the data item, the pattern matchingprocess may “state leap” to a separate pattern class table whichincludes only those patterns relevant to the particular type of HTTPmessage. Thereafter, the remaining portion of the data item may beprocessed using the selected pattern class table until the data item iscompletely processed, or until another “state leap” to a different classtable is triggered. As described in more detail hereinafter, a speed andefficiency with which pattern matching is performed can be greatlyimproved by dynamically state leaping between various pattern classesduring a pattern matching process since each pattern class generallyrepresents a smaller and less complex set of patterns relative acomplete set of input patterns. In many cases, partitioning a set of Rregular expressions with a regular expression table of size S into twotables of R/2 regular expressions may yield tables of sizes S/10 orsmaller.

FIG. 10 illustrates an example of an enhanced pattern state table, whereeach table entry includes a new callback function identifier field.Similar to the pattern state table 610 of FIG. 6, each table entry inthe pattern state table 1010 comprises a number of fields including acurrent state 1012, an input value 1014, a next state 1016, and a matchidentifier field 1018. In one embodiment, the pattern state table 1010further includes a callback function identifier field 1020. In general,a DPI microservice or other process may use the pattern state table 1010to detect patterns in input data items in a manner similar to that ofpattern state table 1010; however, in addition to the ability todetermine whether a current state corresponds to a pattern match (e.g.,based on the match identifier field 1018), each table entry may furtherindicate whether to invoke a particular “callback function.” In anembodiment, a callback function generally enables a pattern matchingprocess to dynamically switch to one of a plurality of different patternclass state tables if one or more specified conditions are met. Thecallback function identifier field 1020 may itself specify one or moretransition conditions and a pattern class state table for thetransition, or the callback identifier field may reference a separateexternal function that determines whether to transition to a separatepattern class state table.

FIG. 11 is a flow diagram illustrating an example process for generatinga master pattern matching table, an alternative master pattern matchingtable, and a plurality of class pattern matching tables. At block 1102,a separate pattern matching table is generated for each pattern of a setof input patterns. In an embodiment, the set of input patterns mayrepresent all patterns for which a DPI microservice is to search for ininput data items, and which may span different types of input data itemsand data item contexts. For example, the set of input patterns mayinclude patterns for detecting various types of malicious networktraffic, spam and other unwanted email messages, virus signatures, andso forth. A separate pattern matching table may be generated for eachpattern, for example, as illustrated by the pattern state table 610 forthe pattern 602.

At block 1104, a master pattern matching table is generated by mergingall of the individual pattern matching tables created at block 1102. Forexample, a merged pattern matching table may be created by merging allof the states and state transitions comprising each of the individualpattern matching tables created at block 1102 into a single “master”pattern matching table.

At block 1106, an alternative master pattern matching table is generatedby merging a subset of the pattern matching tables generated at block1102. For example, instead of merging every pattern matching tablegenerated at block 1102, as in block 1104, only a selected subset ofpatterns may be merged into an alternative master pattern table. Theselected subset of patterns may, for example, correspond to a set of“top-level” patterns each of which identifies an initial pattern classfor state leaping. Additional details related to the creation and use ofan alternative master pattern matching table are described hereinafterin Section 3.4.

At block 1108, a class entry mask is set for each pattern class of oneor more pattern classes. For example, referring again to FIGS. 4, 5,each pattern entry (e.g., each of pattern entries 410-420) may include apattern class list indicating one or more classes to which the patternbelongs. Furthermore, a separate class table (e.g., class table 502) mayinclude one or more pattern class entries, where each pattern classentry represents a defined pattern class. For a specific pattern class,a pattern table corresponding to those patterns belonging to thatpattern class can be used when the conditions of the class entry maskhave been met.

In one embodiment, each pattern class entry in a pattern class tableincludes a class entry mask (e.g., a class entry mask 514 for thepattern class entry 510), where the class entry mask specifies one ormore patterns which, when matched during a pattern matching process,indicates that an associated class callback function is to be invoked.For example, if a “FRUITS” pattern class represents a pattern classincluding each of the patterns “PEAR”, “ORANGE”, and “APPLE”, a classentry mask for the class may be the pattern “FRUIT”. In this example, ifthe pattern “FRUIT” is matched when processing a particular data item,then a callback function associated with the “FRUIT” pattern class maybe invoked, where the callback function may determine whether a stateleap transition to the “FRUIT” pattern class matching is to occur. Thus,the class “FRUIT” (including the patterns “PEAR”, “ORANGE” and “APPLE”)may be used for pattern matching when the pattern mask “FRUIT” isdetected in an input stream. Those patterns (including any memory spacerequired for those patterns in a pattern table) are not required untilthe conditions of that class entry mask are met.

At block 1110, for each pattern class, a pattern class matching table isgenerated by merging each of the pattern matching tables for patternsbelonging to pattern class. Referring again to the example above of a“FRUITS” pattern class including each of the patterns “PEAR”, “ORANGE”,and “APPLE”, a pattern class matching table may be generated by mergingtogether each of the individual pattern matching tables generated forthe patterns “PEAR”, “ORANGE”, and “APPLE” at block 1102.

FIG. 12 is a flow diagram illustrating an example process for performingpattern matching using a master pattern matching table, an alternativemaster pattern matching table, and/or a plurality of class patternmatching tables. For example, the process described in FIG. 12 may use aset of pattern matching tables as generated by the example processdescribed above in reference to FIG. 11.

At block 1202, a next input character is received. Similar to theprocess described in reference to FIG. 7, if a particular input dataitem processed comprises a HTTP request message including the line“GET/pub/WWW/TheProject.html HTTP/1.1”, for example, the data item maybe processed by first receiving the next character “G”, followed by thenext character “E”, followed by the next character “T”, followed by aspace character, and so forth, where a next character is received ateach iteration of the example steps depicted by blocks 1202-1212.

At block 1204, based on the next character input received at block 1202and a current state, a next state is identified in the current patternmatching table. In an embodiment, a “current” pattern matching table mayrefer to any of the master pattern matching table, alternative masterpattern matching table, or any one of the plurality of class patternmatching tables. For example, a pattern matching process may start byinitially using a master pattern matching table or alternative masterpattern matching table. As described in the subsequent steps, based ondetecting a pattern that is further determined to match one or moreclass entry masks, the pattern matching process may dynamically stateleap to a different pattern matching table for the purposes ofprocessing any remaining portion of the data item.

At block 1206, if the next state corresponds to a pattern match, theoccurrence of the matched pattern is signaled. For example, in referenceto the example pattern state table 610, the occurrence of a matchedpattern may be signaled if the “match” field is set to “yes” for thenext state identified in block 1204. In an embodiment, signaling amatched pattern may include setting a match flag, incrementing acounter, adding the matched pattern to a matched pattern list, causingdisplay of one or more visual alerts, and/or performing any otherprocesses.

At block 1208, any patterns determined to match at block 1206 arecompared to the set of class entry masks (e.g., the set of class entrymasks set at block 1108 in FIG. 11). For example, if the next stateidentified at block 1204 is determined to represent a pattern match atblock 1206, the matched pattern may be compared against each of theclass entry masks (e.g., class entry masks 514 . . . 524) of a classtable 502.

At block 1210, if any of the matched patterns are determined to match aclass entry mask at block 1208, a pattern class matching tableassociated with the matched class entry mask is set as the currentpattern matching table. For example, if the master pattern matchingtable, alternative master pattern matching table, or a particular classpattern matching table was previously set as the current patternmatching table, and if the particular class entry mask is determined tomatch at block 1206, the corresponding class pattern matching table maythen be used to search for patterns in any remaining portion of the dataitem.

In an embodiment, a process for setting a new pattern matching table asthe current table, or performing a “state leap,” may involve any numberof processes. For example, setting a new pattern matching table as thecurrent table may involve removing the current table from memory, andloading the new pattern matching table into memory. In this manner, anoften smaller and more efficient class pattern matching table may beloaded into memory and used when particular class entry mask pattern isencountered in first portions of a data item, where any remainingportion of the data item may be processed using only the class patternmatching table unless a subsequent “state leap” condition isencountered.

At block 1212, the current state is updated. For example, based onidentifying a next state by identifying an appropriate entry in thecurrent pattern matching table at block 1204, the current state may beset to the identified next state for subsequent processing steps. Asdepicted in FIG. 12, the process illustrated in blocks 1202-1208 may berepeated until there are no additional input characters, until one ormore particular patterns are matched, or until any other definedconditions are met.

Referring again to the example of a pattern matching process including a“FRUIT” pattern class, the process may begin searching for patterns inan input data item using a master pattern matching table, where the dataitem is processed starting from the beginning of the data item andproceeding to the end of the data item. In a hypothetical example, thepattern matching process may determine that the pattern “FRUIT” ismatched at a location approximately 10% of the way into processing thedata item, and the process may further determine that the “FRUIT”pattern matches the class entry mask for the “FRUITS” pattern class.Based on matching the “FRUITS” class entry mask, the “FRUITS” patternclass matching table is set to the current pattern matching table andthe remaining 90% of the file may be processed using the “FRUITS”pattern class matching table instead of the master pattern matchingtable. If during processing the remaining 90% of the file another classentry mask is matched, yet another pattern class matching table may beselected to process any remaining portion of the file, and so forth. Inthis manner, the pattern matching process may selectively “state leap”between various pattern class matching tables depending on a pattern“context” detected at earlier portions of the data item, therebypotentially reducing the number of patterns to be matched andaccelerating the overall pattern matching process.

To further illustrate an example relationship between a pattern tableand a pattern class table, FIG. 13 illustrates an example “master”pattern table 1302, and FIG. 14 illustrates an example pattern classtable 1402. For example, master pattern table 1302 comprises twelve (12)different patterns labeled 1310-1332, and pattern class table 1402comprises three (3) pattern class entries 1410, 1430, and 1450. Themaster pattern table 1302 and pattern class table 1402 each depict alimited number of entries for illustrative purposes only; actualembodiments may include any number of entries in each table.

In an embodiment, master pattern table 1302 includes several patternentries (e.g., entries 1310-1326), each of which is associated with aparticular class. For example, each of the entries 1310-1314(corresponding to the patterns “APPLE”, “PEAR”, and “ORANGE”,respectively) is associated with a “FRUIT” pattern class. Similarly,each of the entries 1316-1320 (corresponding to the patterns “FORD”,“TOYOTA”, and “BMW”, respectively) is associated with a “CAR” patternclass. Each of the entries 1322-1326 (corresponding to the patterns“UNITED”, “AMERICAN”, and “DELTA”, respectively) is associated with an“AIRLINE” pattern class.

In an embodiment, the master pattern table 1302 further includes three(3) patterns, 1328-1332, which are not associated with any class. Forexample, each of the pattern entries 1328-1332 is associated with alabel (e.g., “FRUIT”, “CAR”, and “AIRLINE”), but with a class identifierof “none”. In this example, each of the patterns in the master patterntable which do not belong to any particular class may represent“top-level” patterns and which serve as “gateway” patterns to one ormore class tables. In the example of FIGS. 13, 14, the pattern tableentry 1328 corresponding to the pattern “FRUIT” also corresponds to theclass entry mask for the “FRUIT” pattern class entry 1410. Thus, if apattern matching process begins processing a data item using the masterpattern table 1302, and the “FRUIT” pattern table entry 1328 is found inthe data item, the process may then match the “FRUIT” pattern againstthe class entry mask for the pattern class entry 1410 and select theclass pattern matching table associated with the pattern class entry1410 as the current pattern matching table. Although in the example ofFIGS. 13, 14 each of the “top-level” pattern entries 1328-1332corresponds directly to one of the class entry masks from the patternclass table 1402, in other examples, some “top-level” patterns may notdirectly match any class entry mask.

FIG. 14 illustrates a corresponding pattern class table 1402. Asdepicted in the example of FIG. 14, a pattern class table 1402 includesthree (3) separate pattern class entries 1410-1450. In an embodiment,each class entry includes a name, a class entry mask, a class callbackfunction, and a pattern list. For example, pattern class entry 1410includes a class name 1412 with the associated label “FRUIT”, a classentry mask 1414 specifying the pattern “FRUIT”, a class callbackfunction 1416, and a pattern name list 1418 (e.g., identifying thepatterns “APPLE”, “PEAR”, and “ORANGE”).

As described above, each pattern class entry in the pattern class table1402 includes a class entry mask. For example, the pattern class entry1430 named “CARS” includes a class entry mask specifying the pattern“CAR”. Thus, for a pattern matching process using this pattern matchingtable (e.g., pattern state table 1010) including the callback functionidentifier field, as a data item is parsed, if the pattern “CAR” ismatched in a data item, the matching state may identify a class callbackfunction. In this example, the class callback function is the “CAR”class callback function 1436. In an embodiment, the “CAR” class callbackfunction may be configured to perform any number of operationsincluding, for example, confirming that the “CAR” pattern was matched,determining whether one or more other particular patterns werepreviously matched, determining whether one or more other particularpatterns have not previously matched, determining a location within thedata item where the pattern was matched, etc.

In an embodiment, the “CAR” class callback function 1436 may be furtherconfigured to state leap to a particular pattern class matching table(e.g., to a “CARS” pattern class matching table) if one or moreconditions are met, as described above. Referring again to the “CARS”pattern class example above, in response to a state leap to the “CARS”pattern class matching table, instead of processing any remainingportion of the data item by searching for patterns from the masterpattern table 1302, a more specific pattern class matching tablecomprising only the patterns “FORD”, “TOYOTA”, and “BMW” may be used.

To further illustrate a pattern matching process utilizing the masterpattern table 1302 and pattern class table 1402, assume that a DPImicroservice receives a document, and it known that the subject matterof the document pertains to one of airlines, cars, and fruits. Morespecifically, assume that the subject matter of the document relates toa specific type of either airlines, cars, or fruits. For example, thesubject matter of the document may relate airlines, and morespecifically to Delta airlines. If the document is parsed from beginningto end, it is likely that one of the “top-level” class patterns (e.g.,“FRUIT”, “CAR”, or “AIRLINE”) may be encountered early in the document,and the remainder of the document may likely contain one or more of thepatterns associated specifically with the encountered pattern class.Thus, when an entry mask corresponding to a pattern class is encounteredin the document, the pattern matching process can focus on the subset ofpatterns associated with the class, and the rest of the patterns can beignored. Furthermore, any number of levels in a pattern class hierarchymay be specified. For example, referring again to FIG. 13, a subclassmay be generated for the pattern “FORD” and include a set of patternsincluding “EXPLORER”, “PINTO”, and “TAURUS”, and so forth.

Although many of the examples described above use common words forpatterns, similar techniques may be used to process classes of networktraffic and other data comprising other types of patterns. For example,in a network security system, one particular class may include patternsof interest to be found in HTTP messages, another class may includepatterns to be found in FTP messages, and yet another class may includepatterns to be found IMAP messages, etc. In this example, if a classentry mask for the HTTP class is encountered near the beginning of aninput data item, the remainder of the data item can be analyzed usingonly the patterns from the HTTP class, and excluding all of the patternsfrom the FTP class, IMAP class, and other classes comprising patternsunrelated to HTTP messages. If the patterns included in the HTTP classrepresent only a small percentage of the total number of patterns, forexample, and detection of the HTTP pattern class occurs relatively earlyin processing the data item (e.g., as a result of detecting one or morepatterns present in most or all HTTP messages), the vast majority ofpatterns can be ignored for most of the pattern matching process.

It is noted that the processes described above may result in somepatterns being missed when processing a data item. For example, amalformed data item may initially appear to be one type of data, butactually contain other types of data (e.g., a malformed network messageheader may cause one type of network message to appear as another).Referring to the example pattern table and pattern class table depictedin FIGS. 13 and 14, a data item may initially include a patterncorresponding to one class (e.g., “FRUIT”), however, the remainingportion of the data item may contain content more specifically relatedto cars and not types of fruit. In these instances, the master patterntable can be used as backup to check for missed patterns. For example,if a pattern class is matched in a first portion of a data item, but nopattern class-specific patterns are matched in a remaining portion ofthe data item, the data item may be re-checked without state leaping tothe particular pattern class to determine whether other patterns mayhave been missed as a result of the state leap.

FIG. 15 illustrates an example DPI microservice that includes componentsfor enhanced regular expression matching, as illustrated in the examplesdescribed above. In an embodiment, a DPI microservice 1510 comprises apattern processor 320, a master pattern matching table 1530, analternative master pattern matching table 1532, one or more patternclass matching tables 1524-1528, a current pattern matching table 322, apattern table 1540, and a class table 1542.

In one embodiment, a pattern processor 320 represents a process forperforming enhanced pattern matching functions. A pattern processor 320may, for example, search for patterns in input data items using acurrent pattern matching table 1522. As described above in reference tothe example flow diagram of FIG. 12, the current pattern matching table1522 may, at any particular point in time, be one of the master patternmatching table 1530, the alternative master pattern matching table 1532,or one of the pattern class matching tables 1524-1528. For example, apattern processor 320 may start processing a data item using the masterpattern matching table 1530 (or alternative master pattern matchingtable 1532), and may subsequently state leap to any of the pattern classmatching tables 1524-1528 in response to matching one or more particularpatterns from the current pattern matching table 1522 in the input dataitem.

3.4. Alternative Pattern Matching Tables

In one embodiment, in addition to or instead of a master patternmatching table, a DPI microservice may generate and use an “alternative”master pattern matching table for use during pattern matching processes.At a high level, an alternative master pattern matching table maycomprise a subset of the patterns included in a master pattern matchingtable, where the selected subset of patterns corresponds to a set of“top-level” patterns in a pattern class hierarchy.

For example, a master pattern matching table may include a large numberof entries representing all possible patterns to be searched. However,if a set of pattern class matching tables have been generated, it may bemore efficient to begin a pattern matching process with a set ofpatterns representing a “top-level” of the class hierarchy, for example,a set of patterns which are not a member of any particular class, butwhich may lead to one or more of the pattern class tables. For example,in reference to FIG. 13, a separate alternative master pattern matchingtable may be generated for the pattern entries 1328-1332, each of whichis not a member of any pattern class (as indicated by the class value of“none”). In this example, a DPI microservice can process a data item byinitially searching the data item for only those three patterns. If oneof those three patterns is found in the data item, the DPI microservicecan then state leap to a corresponding pattern class matching table.

In one embodiment, a master pattern table may be programmaticallypartitioned into an alternative pattern matching table and one or morepattern class tables. For example, a DPI microservice or other componentmay monitor the pattern matching process over a set of sample dataitems. Based on the monitoring, the DPI microservice may track whichpatterns and how often particular patterns from the master pattern tableare matched, including an order in which patterns are matched relativeto one another. For example, a profiling component may determine whichpatterns are matched most frequently for particular types of data items(e.g., one set of patterns may frequently match when the data item is anemail message, while another set of patterns frequently match when thedata item is an HTTP request message, etc.), and also which patternsserve as “gatekeepers” to other patterns (e.g., certain patterns maymatch only if one or more other patterns previously matched in the samedata item). This information can then be used to determine classgroupings, where frequently co-occurring patterns are grouped intoclasses and “gatekeeper” patterns serve as class entry masks forparticular classes. One of the top-level “gatekeeper” patterns may thenbe used to form an initial alternative master pattern matching table, asdescribed above.

3.3. Pattern Matching Using Pattern Functions

The examples described in the preceding sections relate generally totechniques for determining the presence of one or more patterns in dataitems. As indicated, this process may involve searching for the patternsby receiving one character, byte, or other element of the data item at atime starting from the beginning of the data item and proceeding to theend of the data item, and determining whether any portion of thereceived input sequence matches any of the patterns of interest. Dataitems generally may be processed in this way because many patternspotentially can be found at any location within the data item. Forexample, one pattern matching process may search for a number of wordsand phrases which occur frequently in spam email messages, where thepatterns may be found in any of the body of the email message, thesubject line, the email message header fields, etc.

However, some patterns may specify a sequence of tokens which may befound, if present at all within a given data item, only at one or moreparticular locations within the data item. For example, one particularpattern may specify a sequence of tokens that can be found, if presentat all, only as the value for a particular field of a particular type ofnetwork message, where the particular field generally is found at thesame location within each instance the particular type of networkmessage. In one embodiment, such patterns specifying one or moreparticular locations within data items where the associated sequences oftokens may be found are referred to herein as “fixed offset” patterns.For example, a “fixed offset” pattern may include syntax indicating oneor more particular locations within a data item (e.g., 10 charactersfrom the beginning of the data item, within the last 50 bytes of thedata item, etc.) where the pattern may be found.

According to one embodiment, to accelerate pattern matching data itemsagainst sets of patterns which include one or more fixed offsetpatterns, one or more “pattern functions” may be generated for the fixedoffset patterns. At a high level, a pattern function represents a codesegment, script, or any other executable instructions configured todetermine whether a particular pattern exists at one or particularlocations within a data item. In particular, each pattern function mayuse one or more processes other than regular expression matching thepattern against the entire data item to determine whether the patternexists in the data item. For example, a pattern function may useinformation about the location of the fixed offset to perform a directstring comparison or other similar function directly against the data atthe one or more fixed offset location within the data item.

By processing the fixed offset patterns against only the fixed offsetlocations in the data item, a determination of the patterns presence inthe data item typically can be performed significantly faster than ifthe pattern was included as part of a larger regular expression matchingprocess. Furthermore, by removing patterns determined to include a fixedoffset from the overall set of patterns to be matched, the size of thepattern matching tables generated for other variable offset patterns canbe reduced, thereby accelerating the regular expression matching orother processes used to pattern match the variable offset patterns.

FIG. 16 is a block diagram illustrating an example function table. In anembodiment, a function table 1602 includes one or more function tableentries 1610-1620. Each entry may comprise a pattern name, patternoffset, and pattern function. For example, the pattern entry 1610includes a pattern name 1612 (e.g., which may match a pattern name froma corresponding pattern table), a pattern offset 1614 (e.g., identifyingone or more locations in a data item where the associated pattern mayexist), and a pattern function 1616 (e.g., identifying a functionconfigured to compare the associated pattern against data located at thepattern offset locations within data items to determine whether thepattern exists). For example, a DPI microservice may generate a functiontable based on identifying one or more fixed offset patterns from a setof patterns, as described below in reference to FIG. 17.

FIG. 17 is a flow diagram illustrating an example process for generatingone or more pattern functions and one or more separate pattern matchingtables from a set of patterns. At block 1702, an initial set of patternsis partitioned into a set of fixed offset patterns and a set of variableoffset patterns. As indicated above, a “fixed offset” pattern generallyrefers to a pattern including an identifier of a particular location ina data item, a set of locations within in a data item, one or morelocation ranges within a data item, or any other location specificsyntax. As one example, one fixed offset pattern may specify a searchfor the sequence of tokens “HOST” appearing seven (7) characters fromthe beginning of a data item. For example, a particular network protocolmay specify that validly formatted protocol messages include a “HOST”field at a location in the message that begins seven (7) characters fromthe beginning of the data. As another example, a fixed offset patternmay specify a search for a range of IP addresses at a location that iseither five (5) characters from the beginning or twenty (20) charactersfrom the beginning of a data item. As yet another example, a fixedoffset pattern may specify a search for email addresses which appearwithin the first fifty (50) characters of data items, only within thelast one hundred characters, only within the range of charactersstarting at 200 and ending at 400 or the range of characters starting at600 and ending at 800, etc.

In one embodiment, the set of fixed offset patterns may be identifiedusing a pattern matching process against the initial set of patterns.For example, a regular expression matching process may be used to searchthe patterns for those patterns which include one or more syntaxelements indicating that the pattern may be located at one or more fixedlocations within data.

FIG. 18 illustrates separate examples of searching for a variable offsetpattern and a fixed offset pattern in a data item. For example, avariable offset pattern 1802 indicates that the pattern “PEAR” may existat any location in a date item (e.g., because the pattern syntax doesnot specify any fixed offset location information). The data item 1804,for example, illustrates a data item where the string “PEAR” exists atan arbitrary location within the data item. As described in reference toFIG. 9, in one embodiment, a pattern matching table 1806 may begenerated for the variable offset pattern 1802 and used to search thedata item 1804, character-by-character from the beginning of the dataitem to the end, for the presence of the pattern “PEAR” (and possiblymany other patterns at the same time).

As another example, a fixed offset pattern 1812 specifies a search forthe pattern “PEAR”, but further specifies that the pattern is to bematched against the data beginning at line 2, character 7, of input dataitems. For example, the data item 1814 may represent a structured orsemi-structured data item including a “TYPE” field, where a value forthe “TYPE” field typically is present at line 2, character 7 for similartypes of data items. In this instance, instead of using a patternmatching table to process the data item 1814 character-by-character frombeginning to end to determine the presence of the pattern “PEAR”, apattern function 1816 may be used to perform the same operation moreefficiently. For example, a pattern function 1816 may be configured toperform a direct comparison of the data located at line 2, character 7,in the data item against the pattern “PEAR”, and the function can ignorethe remainder of the data item for the purposes of matching thatparticular pattern. Processing the fixed offset pattern in this mannermay represent a significant efficiency improvement as compared to animplementation where the fixed offset pattern is included in a masterpattern matching table which matches every pattern against the entiredata item.

Returning to FIG. 17, at block 1704, a pattern matching table isgenerated for each variable offset pattern. For example, similar to theprocess described in reference to FIG. 9, for each of the patterns notidentified as a fixed offset pattern in block 1702, a separate patternmatching table may be generated. In one embodiment, each of the variableoffset patterns may be expressed using regular expressions, and thegenerated pattern matching tables may be regular expression matchingtables.

At block 1706, a pattern function is generated for each one or morefixed offset patterns. In an embodiment, a pattern function generallyrepresents any type of executable code, script, etc., which enables aDPI microservice or other processing component to determine whether oneor more particular patterns exist in an input data item. For example, apattern function may comprise an executable code segment writtenspecifically for performing the pattern matching using one or moretechniques instead of or in addition to regular expressing matching.

As one particular example, one pattern function may comprise a segmentof code written in the C programming language which receives a dataitem, moves a pointer to a particular offset within the data item, andperforms a direct evaluation of the portion of the data item at theparticular offset to the pattern. If the code segment determines thatthe pattern and the portion of the data item are the same, it can returna positive match indicator; otherwise, the code segment may return anegative match indicator. In general, a pattern function may compriseany code segment, and may utilize any existing code (e.g., operatingsystem functions), to perform the comparison. Each fixed offset patternmay be associated with a separate pattern function, or one or morepattern functions may be configured to search for the presence of two ormore separate fixed offset patterns.

In one embodiment, a pattern function is generated programmaticallythrough regular expression parsing of the pattern functions. Forexample, patterns containing offsets or anchors that restrict the matchof a pattern to a subset of a data stream can be identified throughmatching the regular expression syntax used to specify offsets andanchors. Further filtering may be used to identify those patterns thatcan be matched using a numeric evaluation (characters at some offsetwill be a number that can be parsed using standard C string functions)or substring evaluation (alphanumeric characters of defined length withno variable characters).

At block 1708, the pattern matching tables of variable offset patternsare merged into a master pattern table. For example, similar to block1104 in FIG. 11, a master pattern matching table may be generated bymerging all of the states and state transitions comprising each of theindividual pattern matching tables created at block 1704. In anembodiment, an alternative master pattern matching tables and one ormore pattern class tables may be further generated.

FIG. 19 depicts a flow diagram illustrating an example process forpattern matching data items using regular expression matching to searchfor a set of variable offset patterns and separately using patternfunctions to search for a set of fixed offset patterns.

At block 1902, a data item is received. For example, a DPI microservice1510 may receive one or more network messages, email messages, files, orother type of data item. In an embodiment, the DPI microservice 1510 mayreceive an entire data item, or receive the data item as an input streamand accessed from an input buffer.

At block 1904, first pattern results are generated by processing thedata item using each of the created pattern functions. As describedabove, each of the pattern functions may be configured to determinewhether the data item includes one or more patterns at one or more fixedoffset locations. In an embodiment, each of the patterns functions mayperform the matching using any number of different processes, includingdirect data comparisons, string matching using operating systemfunctions, etc. The first pattern results may include zero or morepatterns determined to exist in the data item based on executing thepattern functions against the data item.

At block 1906, second pattern results are generated based on processingthe data item using a regular expression pattern matching process tosearch for the variable offset patterns. In an embodiment, a DPImicroservice 1510 may use one or more of the master pattern matchingtable, alternative master pattern matching table, and/or pattern classmatching tables generated in FIG. 17 to perform the regular expressionpattern matching. The second pattern results similarly may include zeroor more patterns from the pattern matching tables determined to exist inthe data item. In an embodiment, the second pattern results may begenerated at any time relative to generation of the first patternresults. For example, the second pattern results may be generatedbefore, after, or concurrently with the generation of the first patternresults. Generating the first pattern results and the second patternresults in parallel, for example, may increase a speed with which thepattern results are generated.

At block 1908, the first pattern results and the second pattern resultsare merged to create a third pattern results. For example, all of thezero or more patterns matched based on the pattern functions, and all ofthe zero or more the patterns matched based on the regular expressionmatching, may be merged into a third pattern result set.

At block 1910, one or more actions are performed relative to thereceived data item based on the third pattern results. For example,based on detecting the presence of one or more particular patterns fromthe third pattern result set, the data item may be dropped, rejected,deleted, quarantined, or processed in any other manner.

4.0. Example Embodiments

Examples of some embodiments are represented, without limitation, in thefollowing numbered clauses:

In an embodiment, a method or non-transitory computer readable mediumcomprises: searching a data item using a first pattern matching table;determining that one or more first patterns of the first patternmatching table exist in a first portion of the data item; in response todetermining that the one or more first patterns of a first patternmatching table exist in a first portion of the data item, selecting asecond pattern matching table from a plurality of pattern matchingtables; searching a second portion of the data item for patterns usingthe second pattern matching table, wherein the second portion of thedata item does not include the first portion of the data item;determining that one or more second patterns of the second pattern tableexist in the second portion of the data item; performing an actionrelative to the data item based at least in part on the determinationthat the one or more first patterns exist in the first portion of thedata item and the one or more second patterns exist in the secondportion of the data item.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the first pattern matching is a regular expressiontable, and wherein determining that the one or more first patterns ofthe first pattern matching table exist in the first portion of the dataitem includes regular expression matching.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the second portion of the data is not comparedagainst the one or more first patterns of the first pattern matchingtable.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the second pattern matching table is selected by acallback function associated with the first pattern matching table.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the first pattern matching table comprises aplurality of entries, each entry of the plurality of entries specifyinga current state value, an input value, a next state value, a matchindicator, and a callback function identifier; wherein the secondpattern matching table is selected by a callback function identified bya callback function identifier in the first pattern matching table.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein determining that the one or more first patterns ofthe first pattern matching table exist in the data item comprises:wherein the first pattern matching table comprises a plurality ofentries, each entry of the plurality of entries specifying a currentstate value, an input value, a next state value, a match indicator, anda callback function identifier; receiving a next input value from thedata item; based on a current state value and the next input value,identifying an entry in the first pattern table, the entry including aparticular callback function identifier; wherein the second patternmatching table is selected by a callback function corresponding to theparticular callback function identifier.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the second pattern matching table contains less thanall of the patterns contained in the first pattern matching table.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each of the one or more first patterns is differentfrom each of the one or more second patterns.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item comprises character-based data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item comprises one or more of: anapplication protocol message, a network protocol message, an emailmessage, a file.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item is received by a deep packet inspection(DPI) microservice.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item is received by a deep packet inspection(DPI) microservice, and wherein the DPI microservice comprises asoftware container.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one pattern of the one or more firstpatterns is expressed using a regular expression.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one pattern of the one or more firstpatterns is expressed using a regular expression; and wherein the firstpattern matching table comprises one or more entries, each entry of theone or more entries representing a state of processing at least oneregular expression.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the first pattern matching table is a master patterntable comprising entries corresponding states for all input patterns.

In an embodiment, a method or non-transitory computer readable mediumcomprises: generating, based on a plurality of input patterns, a masterpattern table comprising states for the plurality of input patterns;generating, based on the plurality of input patterns, an alternativepattern table comprising states for a selected subset of the pluralityof input patterns; wherein the first pattern matching table is thealternative pattern table.

In an embodiment, a method or non-transitory computer readable mediumcomprises: in response to determining that the one or more secondpatterns of the second pattern table exist in the second portion of thedata item, selecting a third pattern matching table from the pluralityof pattern tables; determining that one or more third patterns of thethird pattern matching table exist in a third portion of the data item,wherein the third portion of the data item is not compared againsteither the one or more first patterns or the one or more secondpatterns.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the action comprises one or more: dropping the dataitem, rejecting the data item, deleting the data item, quarantining thedata item.

In an embodiment, a method or non-transitory computer readable mediumcomprises: generating one or more first pattern results, the firstpattern results indicating that one or more first patterns of a firstset of patterns were determined to exist in a data item based on regularexpression matching the one or more first patterns against the dataitem; generating one or more second pattern results, the one or moresecond pattern results indicating that one or more second patterns of asecond set of patterns were determined to exist in the data item basedon processing the data item by applying one or more pattern functions tothe data item; merging the first pattern results and the second patternresults to create third pattern results; performing an action relativeto the data item based at least in part on the third pattern results.

In an embodiment, a method or non-transitory computer readable mediumcomprises: partitioning a set of input patterns into the first set ofpatterns and the second set of patterns; wherein the first set ofpatterns includes patterns from the set of input patterns determined tonot include a pattern element specifying a fixed offset.

In an embodiment, a method or non-transitory computer readable mediumcomprises: partitioning a set of input patterns into the first set ofpatterns and the second set of patterns; wherein the second set ofpatterns includes patterns from the set of input patterns determined toinclude a pattern element specifying fixed offset.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each pattern function of the one or more patternfunctions involves determining whether one or more particular patternsexist in data items without performing regular expression matching.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one pattern function of the one or morepattern functions involves determining whether one or more particularpatterns exist in data items based on a string comparison function.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the generating the one or more first pattern resultsfurther comprises: wherein one or more particular first patterns of thefirst patterns are specified in a first pattern table; in response todetermining that the one or more particular first patterns exist in afirst portion of the data item, selecting a second pattern table from aplurality of pattern tables; determining whether one or more secondpatterns of the second pattern table exist in a second portion of thedata item, wherein the second portion of the data item is not comparedagainst the one or more particular first patterns of the first patterntable; wherein the first pattern results include the first particularpatterns and the second patterns.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item comprises character-based data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item comprises one or more of: anapplication protocol message, a network protocol message, an emailmessage, a file.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item is received by a deep packet inspection(DPI) microservice.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the data item is received by a deep packet inspection(DPI) microservice, and wherein the DPI microservice comprises asoftware container.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the action comprises one or more: dropping the dataitem, rejecting the data item, deleting the data item, quarantining thedata item.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the one or more first pattern results are generatedconcurrently with generating the one or more second pattern results.

Other examples of these and other embodiments are found throughout thisdisclosure.

5.0. Implementation Mechanism—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination thereof. Such special-purpose computing devices may alsocombine custom hard-wired logic, ASICs, or FPGAs with custom programmingto accomplish the techniques.

FIG. 20 is a block diagram that illustrates a computer system 2000utilized in implementing the above-described techniques, according to anembodiment. Computer system 2000 may be, for example, a desktopcomputing device, laptop computing device, tablet, smartphone, serverappliance, computing mainframe, multimedia device, handheld device,networking apparatus, or any other suitable device.

Computer system 2000 includes one or more buses 2002 or othercommunication mechanism for communicating information, and one or morehardware processors 2004 coupled with buses 2002 for processinginformation. Hardware processors 2004 may be, for example, generalpurpose microprocessors. Buses 2002 may include various internal and/orexternal components, including, without limitation, internal processoror memory busses, a Serial ATA bus, a PCI Express bus, a UniversalSerial Bus, a HyperTransport bus, an Infiniband bus, and/or any othersuitable wired or wireless communication channel.

Computer system 2000 also includes a main memory 2006, such as a randomaccess memory (RAM) or other dynamic or volatile storage device, coupledto bus 2002 for storing information and instructions to be executed byprocessor 2004. Main memory 2006 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 2004. Such instructions, whenstored in non-transitory storage media accessible to processor 2004,render computer system 2000 a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 2000 further includes one or more read only memories(ROM) 2008 or other static storage devices coupled to bus 2002 forstoring static information and instructions for processor 2004. One ormore storage devices 2010, such as a solid-state drive (SSD), magneticdisk, optical disk, or other suitable non-volatile storage device, isprovided and coupled to bus 2002 for storing information andinstructions.

Computer system 2000 may be coupled via bus 2002 to one or more displays2012 for presenting information to a computer user. For instance,computer system 2000 may be connected via an High-Definition MultimediaInterface (HDMI) cable or other suitable cabling to a Liquid CrystalDisplay (LCD) monitor, and/or via a wireless connection such aspeer-to-peer Wi-Fi Direct connection to a Light-Emitting Diode (LED)television. Other examples of suitable types of displays 2012 mayinclude, without limitation, plasma display devices, projectors, cathoderay tube (CRT) monitors, electronic paper, virtual reality headsets,braille terminal, and/or any other suitable device for outputtinginformation to a computer user. In an embodiment, any suitable type ofoutput device, such as, for instance, an audio speaker or printer, maybe utilized instead of a display 2012.

One or more input devices 2014 are coupled to bus 2002 for communicatinginformation and command selections to processor 2004. One example of aninput device 2014 is a keyboard, including alphanumeric and other keys.Another type of user input device 2014 is cursor control 2016, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 2004 and for controllingcursor movement on display 2012. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Yetother examples of suitable input devices 2014 include a touch-screenpanel affixed to a display 2012, cameras, microphones, accelerometers,motion detectors, and/or other sensors. In an embodiment, anetwork-based input device 2014 may be utilized. In such an embodiment,user input and/or other information or commands may be relayed viarouters and/or switches on a Local Area Network (LAN) or other suitableshared network, or via a peer-to-peer network, from the input device2014 to a network link 2020 on the computer system 2000.

A computer system 2000 may implement techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 2000 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 2000 in response to processor 2004 executing one or moresequences of one or more instructions contained in main memory 2006.Such instructions may be read into main memory 2006 from another storagemedium, such as storage device 2010. Execution of the sequences ofinstructions contained in main memory 2006 causes processor 2004 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2010.Volatile media includes dynamic memory, such as main memory 2006. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 2002. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2004 for execution. Forexample, the instructions may initially be carried on a magnetic disk ora solid state drive of a remote computer. The remote computer can loadthe instructions into its dynamic memory and use a modem to send theinstructions over a network, such as a cable network or cellularnetwork, as modulate signals. A modem local to computer system 2000 canreceive the data on the network and demodulate the signal to decode thetransmitted instructions. Appropriate circuitry can then place the dataon bus 2002. Bus 2002 carries the data to main memory 2006, from whichprocessor 2004 retrieves and executes the instructions. The instructionsreceived by main memory 2006 may optionally be stored on storage device2010 either before or after execution by processor 2004.

A computer system 2000 may also include, in an embodiment, one or morecommunication interfaces 2018 coupled to bus 2002. A communicationinterface 2018 provides a data communication coupling, typicallytwo-way, to a network link 2020 that is connected to a local network2022. For example, a communication interface 2018 may be an integratedservices digital network (ISDN) card, cable modem, satellite modem, or amodem to provide a data communication connection to a corresponding typeof telephone line. As another example, the one or more communicationinterfaces 2018 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. As yet anotherexample, the one or more communication interfaces 2018 may include awireless network interface controller, such as a 802.11-basedcontroller, Bluetooth controller, Long Term Evolution (LTE) modem,and/or other types of wireless interfaces. In any such implementation,communication interface 2018 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Network link 2020 typically provides data communication through one ormore networks to other data devices. For example, network link 2020 mayprovide a connection through local network 2022 to a host computer 2024or to data equipment operated by a Service Provider 2026. ServiceProvider 2026, which may for example be an Internet Service Provider(ISP), in turn provides data communication services through a wide areanetwork, such as the world wide packet data communication network nowcommonly referred to as the “Internet” 2028. Local network 2022 andInternet 2028 both use electrical, electromagnetic or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on network link 2020 and through communicationinterface 2018, which carry the digital data to and from computer system2000, are example forms of transmission media.

In an embodiment, computer system 2000 can send messages and receivedata, including program code and/or other types of instructions, throughthe network(s), network link 2020, and communication interface 2018. Inthe Internet example, a server X30 might transmit a requested code foran application program through Internet 2028, ISP 2026, local network2022 and communication interface 2018. The received code may be executedby processor 2004 as it is received, and/or stored in storage device2010, or other non-volatile storage for later execution. As anotherexample, information received via a network link 2020 may be interpretedand/or processed by a software component of the computer system 2000,such as a web browser, application, or server, which in turn issuesinstructions based thereon to a processor 2004, possibly via anoperating system and/or other intermediate layers of softwarecomponents.

In an embodiment, some or all of the systems described herein may be orcomprise server computer systems, including one or more computer systems2000 that collectively implement various components of the system as aset of server-side processes. The server computer systems may includeweb server, application server, database server, and/or otherconventional server components that certain above-described componentsutilize to provide the described functionality. The server computersystems may receive network-based communications comprising input datafrom any of a variety of sources, including without limitationuser-operated client computing devices such as desktop computers,tablets, or smartphones, remote sensing devices, and/or other servercomputer systems.

In an embodiment, certain server components may be implemented in fullor in part using “cloud”-based components that are coupled to thesystems by one or more networks, such as the Internet. The cloud-basedcomponents may expose interfaces by which they provide processing,storage, software, and/or other resources to other components of thesystems. In an embodiment, the cloud-based components may be implementedby third-party entities, on behalf of another entity for whom thecomponents are deployed. In other embodiments, however, the describedsystems may be implemented entirely by computer systems owned andoperated by a single entity.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods. In an embodiment, anon-transitory computer readable storage medium, storing softwareinstructions, which when executed by one or more processors causeperformance of any of the foregoing methods.

6.0. Extensions and Alternatives

As used herein, the terms “first,” “second,” “certain,” and “particular”are used as naming conventions to distinguish queries, plans,representations, steps, objects, devices, or other items from eachother, so that these items may be referenced after they have beenintroduced. Unless otherwise specified herein, the use of these termsdoes not imply an ordering, timing, or any other characteristic of thereferenced items.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. In this regard, although specific claim dependencies are setout in the claims of this application, it is to be noted that thefeatures of the dependent claims of this application may be combined asappropriate with the features of other dependent claims and with thefeatures of the independent claims of this application, and not merelyaccording to the specific dependencies recited in the set of claims.Moreover, although separate embodiments are discussed herein, anycombination of embodiments and/or partial embodiments discussed hereinmay be combined to form further embodiments.

Any definitions expressly set forth herein for terms contained in suchclaims shall govern the meaning of such terms as used in the claims.Hence, no limitation, element, property, feature, advantage or attributethat is not expressly recited in a claim should limit the scope of suchclaim in any way. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:generating one or more first pattern results, the first pattern resultsindicating that one or more first patterns of a first set of patternsspecified in a first pattern table were determined to exist in a firstportion of the data item based on regular expression matching the one ormore first patterns against the first portion of the data item, andfurther indicating that one or more second patterns of a second set ofpatterns specified in a second pattern table were determined to exist ina second portion of the data item based on regular expression matchingthe one or more second patterns against the second portion of the dataitem, the generation of the one or more first pattern results including:in response to determining that a first pattern of the first set ofpatterns exists in the first portion of the data item, selecting thesecond pattern table from a plurality of pattern tables, and determiningthat the one or more second patterns specified in the second patterntable exist in the second portion of the data item, wherein the secondportion of the data item is not compared against the one or more firstpatterns specified in the first pattern table; generating one or moresecond pattern results, the one or more second pattern resultsindicating that one or more third patterns of a third set of patternswere determined to exist in the data item based on processing the dataitem by applying one or more pattern functions to the data item; mergingthe first pattern results and the second pattern results to create thirdpattern results; and performing an action relative to the data itembased at least in part on the third pattern results.
 2. The method ofclaim 1, further comprising: partitioning a set of input patterns intothe first set of patterns and the second set of patterns; wherein thefirst set of patterns includes patterns from the set of input patternsdetermined to not include a pattern element specifying a fixed offset.3. The method of claim 1, further comprising: partitioning a set ofinput patterns into the first set of patterns and the second set ofpatterns; wherein the second set of patterns includes patterns from theset of input patterns determined to include a pattern element specifyingfixed offset.
 4. The method of claim 1, wherein each pattern function ofthe one or more pattern functions involves determining whether one ormore particular patterns exist in data items without performing regularexpression matching.
 5. The method of claim 1, wherein at least onepattern function of the one or more pattern functions involvesdetermining whether one or more particular patterns exist in data itemsbased on a string comparison function.
 6. The method of claim 1, whereinthe data item comprises character-based data.
 7. The method of claim 1,wherein the data item comprises one or more of: an application protocolmessage, a network protocol message, an email message, a file.
 8. Themethod of claim 1, wherein the data item is received by a deep packetinspection (DPI) microservice.
 9. The method of claim 1, wherein thedata item is received by a deep packet inspection (DPI) microservice,and wherein the DPI microservice comprises a software container.
 10. Themethod of claim 1, wherein the action comprises one or more: droppingthe data item, rejecting the data item, deleting the data item,quarantining the data item.
 11. The method of claim 1, wherein the oneor more first pattern results are generated concurrently with generatingthe one or more second pattern results.
 12. One or more non-transitorycomputer-readable storage media storing instructions which, whenexecuted by one or more hardware processors, cause performance of:generating one or more first pattern results, the first pattern resultsindicating that one or more first patterns of a first set of patternsspecified in a first pattern table were determined to exist in a firstportion of the data item based on regular expression matching the one ormore first patterns against the first portion of the data item, andfurther indicating that one or more second patterns of a second set ofpatterns specified in a second pattern table were determined to exist ina second portion of the data item based on regular expression matchingthe one or more second patterns against the second portion of the dataitem, the generation of the one or more first pattern results including:in response to determining that a first pattern of the first set ofpatterns exists in a first portion of the data item, selecting thesecond pattern table from a plurality of pattern tables, and determiningthat the one or more second patterns specified in the second patterntable exist in the second portion of the data item, wherein the secondportion of the data item is not compared against the one or more firstpatterns specified in the first pattern table; generating one or moresecond pattern results, the one or more second pattern resultsindicating that one or more third patterns of a third set of patternswere determined to exist in the data item based on processing the dataitem by applying one or more pattern functions to the data item; mergingthe first pattern results and the second pattern results to create thirdpattern results; and performing an action relative to the data itembased at least in part on the third pattern results.
 13. The one or morenon-transitory storage media of claim 12, wherein the instructionswhich, when executed by the one or more computing devices, furthercause: partitioning a set of input patterns into the first set ofpatterns and the second set of patterns; wherein the first set ofpatterns includes patterns from the set of input patterns determined tonot include a pattern element specifying a fixed offset.
 14. The one ormore non-transitory storage media of claim 12, wherein the instructionswhich, when executed by the one or more computing devices, furthercause: partitioning a set of input patterns into the first set ofpatterns and the second set of patterns; wherein the second set ofpatterns includes patterns from the set of input patterns determined toinclude a pattern element specifying fixed offset.
 15. The one or morenon-transitory storage media of claim 12, wherein each pattern functionof the one or more pattern functions involves determining whether one ormore particular patterns exist in data items without performing regularexpression matching.
 16. The one or more non-transitory storage media ofclaim 12, wherein at least one pattern function of the one or morepattern functions involves determining whether one or more particularpatterns exist in data items based on a string comparison function. 17.The one or more non-transitory storage media of claim 12, wherein thedata item comprises character-based data.
 18. The one or morenon-transitory storage media of claim 12, wherein the data itemcomprises one or more of: an application protocol message, a networkprotocol message, an email message, a file.
 19. The one or morenon-transitory storage media of claim 12, wherein the data item isreceived by a deep packet inspection (DPI) microservice.
 20. The one ormore non-transitory storage media of claim 12, wherein the data item isreceived by a deep packet inspection (DPI) microservice, and wherein theDPI microservice comprises a software container.
 21. The one or morenon-transitory storage media of claim 12, wherein the action comprisesone or more: dropping the data item, rejecting the data item, deletingthe data item, quarantining the data item.
 22. The one or morenon-transitory storage media of claim 12, wherein the one or more firstpattern results are generated concurrently with generating the one ormore second pattern results.
 23. An apparatus, comprising: one or morehardware processors; memory coupled to the one or more hardwareprocessors, the memory storing instructions which, when executed by theone or more hardware processors, causes the apparatus to: generate oneor more first pattern results, the first pattern results indicating thatone or more first patterns of a first set of patterns specified in afirst pattern table were determined to exist in a first portion of thedata item based on regular expression matching the one or more firstpatterns against the first portion of the data item, and furtherindicating that one or more second patterns of a second set of patternsspecified in a second pattern table were determined to exist in a secondportion of the data item based on regular expression matching the one ormore second patterns against the second portion of the data item, thegeneration of the one or more first pattern results including: inresponse to determining that a first pattern of the first set ofpatterns exists in a first portion of the data item, selecting thesecond pattern table from a plurality of pattern tables, and determiningthat the one or more second patterns specified in the second patterntable exist in the second portion of the data item, wherein the secondportion of the data item is not compared against the one or more firstpatterns specified in the first pattern table; generate one or moresecond pattern results, the one or more second pattern resultsindicating that one or more third patterns of a third set of patternswere determined to exist in the data item based on processing the dataitem by applying one or more pattern functions to the data item; mergethe first pattern results and the second pattern results to create thirdpattern results; and perform an action relative to the data item basedat least in part on the third pattern results.
 24. The apparatus ofclaim 23, wherein the instructions which, when executed by the one ormore hardware processors, further causes the apparatus to: partition aset of input patterns into the first set of patterns and the second setof patterns; wherein the first set of patterns includes patterns fromthe set of input patterns determined to not include a pattern elementspecifying a fixed offset.
 25. The apparatus of claim 23, wherein theinstructions which, when executed by the one or more hardwareprocessors, further causes the apparatus to: partition a set of inputpatterns into the first set of patterns and the second set of patterns;wherein the second set of patterns includes patterns from the set ofinput patterns determined to include a pattern element specifying fixedoffset.
 26. The apparatus of claim 23, wherein each pattern function ofthe one or more pattern functions involves determining whether one ormore particular patterns exist in data items without performing regularexpression matching.
 27. The apparatus of claim 23, wherein at least onepattern function of the one or more pattern functions involvesdetermining whether one or more particular patterns exist in data itemsbased on a string comparison function.